
'''simple docstring'''
import os
from collections import deque

import torch
from torch.utils.data import Dataset


class 			SCREAMING_SNAKE_CASE__      (     a_):







				def __init__(       self						,				A_=""						,				A_="train"     )->		int:







											'''simple docstring'''






											assert os.path.isdir(_lowercase     )

											UpperCamelCase   			=     []
											UpperCamelCase   			=     os.listdir(_lowercase     )
											for story_filename in story_filenames_list:
																		if "summary" in story_filename:
																									continue
																		UpperCamelCase   			=     os.path.join(_lowercase						,				_lowercase     )
																		if not os.path.isfile(_lowercase     ):
																									continue
																		self.documents.append(_lowercase     )







				def __len__(       self     )->		Any:







											'''simple docstring'''






											return len(self.documents     )







				def __getitem__(       self						,				A_     )->		Optional[Any]:







											'''simple docstring'''






											UpperCamelCase   			=     self.documents[idx]
											UpperCamelCase   			=     document_path.split('/'     )[-1]
											with open(_lowercase						,				encoding='utf-8'     ) as source:
																		UpperCamelCase   			=     source.read()
																		UpperCamelCase   			=     process_story(_lowercase     )
											return document_name, story_lines, summary_lines







def 		A_(     A						:		Optional[Any]):
							UpperCamelCase   			=     list(filter(lambda A: len(a__) != 0		,     [line.strip() for line in raw_story.split('\n')]))

							# for some unknown reason some lines miss a period, add it
							UpperCamelCase   			=     [_add_missing_period(a__) for line in nonempty_lines]

							# gather article lines
							UpperCamelCase   			=     []
							UpperCamelCase   			=     deque(a__)
							while True:
														try:
																					UpperCamelCase   			=     lines.popleft()
																					if element.startswith('@highlight'):
																												break
																					story_lines.append(a__)
														except IndexError:
																					# if "@highlight" is absent from the file we pop
																					# all elements until there is None, raising an exception.
																					return story_lines, []

    # gather summary lines
							UpperCamelCase   			=     list(filter(lambda A: not t.startswith('@highlight')		,     a__))

							return story_lines, summary_lines







def 		A_(     A						:		int):
							UpperCamelCase   			=     [""".""", """!""", """?""", """...""", """'""", """`""", """\"""", """\u2019""", """\u2019""", """)"""]
							if line.startswith('@highlight'):
														return line
							if line[-1] in END_TOKENS:
														return line
							return line + "."







def 		A_(     A						:		int		,     A						:		Tuple		,     A						:		Any):



							if len(a__) > block_size:
														return sequence[:block_size]
							else:
														sequence.extend([pad_token_id] * (block_size - len(a__)))
														return sequence







def 		A_(     A						:		Union[str, Any]		,     A						:		List[Any]):
							UpperCamelCase   			=     torch.ones_like(a__)
							UpperCamelCase   			=     sequence == pad_token_id
							UpperCamelCase   			=     0
							return mask







def 		A_(     A						:		Dict		,     A						:		Tuple		,     A						:		str):
							UpperCamelCase   			=     [tokenizer.encode(a__) for line in story_lines]
							UpperCamelCase   			=     [token for sentence in story_lines_token_ids for token in sentence]
							UpperCamelCase   			=     [tokenizer.encode(a__) for line in summary_lines]
							UpperCamelCase   			=     [token for sentence in summary_lines_token_ids for token in sentence]

							return story_token_ids, summary_token_ids







def 		A_(     A						:		Optional[int]		,     A						:		int):
							UpperCamelCase   			=     []
							for sequence in batch:
														UpperCamelCase   			=     -1
														UpperCamelCase   			=     []
														for s in sequence:
																					if s == separator_token_id:
																												sentence_num += 1
																					embeddings.append(sentence_num % 2)
														batch_embeddings.append(a__)
							return torch.tensor(a__)







import argparse

import torch
from torch import nn

from transformers import MaMaaaConfig, MaMaaaForConditionalGeneration


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						Optional[Any] 							= [
			    """encoder.version""",
			    """decoder.version""",
			    """model.encoder.version""",
			    """model.decoder.version""",
			    """decoder.output_projection.weight""",
			    """_float_tensor""",
			    """encoder.embed_positions._float_tensor""",
			    """decoder.embed_positions._float_tensor""",
			]
			for k in ignore_keys:
						state_dict.pop(a__  ,	a__)


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_							,					a_						:						List[Any] 							= emb.weight.shape
			a_						:						Dict 							= nn.Linear(a__  ,	a__  ,	bias=a__)
			a_						:						str 							= emb.weight.data
			return lin_layer


def 					_UpperCAmelCase				(						a__):

			'''simple docstring'''



			a_						:						Optional[int] 							= torch.load(a__  ,	map_location="""cpu""")
			a_						:						int 							= mam_aaa["""args"""] or mam_aaa["""cfg"""]["""model"""]
			a_						:						Dict 							= mam_aaa["""model"""]
			remove_ignore_keys_(a__)
			a_						:						List[str] 							= state_dict["""encoder.embed_tokens.weight"""].shape[0]

			a_						:						Union[str, Any] 							= MaMaaaConfig(
			    vocab_size=a__  ,	max_position_embeddings=1_0_2_4  ,	encoder_layers=args.encoder_layers  ,	decoder_layers=args.decoder_layers  ,	encoder_attention_heads=args.encoder_attention_heads  ,	decoder_attention_heads=args.decoder_attention_heads  ,	encoder_ffn_dim=args.encoder_ffn_embed_dim  ,	decoder_ffn_dim=args.decoder_ffn_embed_dim  ,	d_model=args.encoder_embed_dim  ,	encoder_layerdrop=args.encoder_layerdrop  ,	decoder_layerdrop=args.decoder_layerdrop  ,	dropout=args.dropout  ,	attention_dropout=args.attention_dropout  ,	activation_dropout=args.activation_dropout  ,	activation_function="""relu"""  ,	)

			a_						:						Union[str, Any] 							= state_dict["""decoder.embed_tokens.weight"""]
			a_						:						List[Any] 							= MaMaaaForConditionalGeneration(a__)
			model.model.load_state_dict(a__  ,	strict=a__)
			a_						:						Any 							= make_linear_from_emb(model.model.shared)

			return model


if __name__ == "__main__":
	__snake_case				:      Union[str, Any]   		=			argparse.ArgumentParser()
	# Required parameters
	parser.add_argument("""fairseq_path""", type=str, help="""path to a model.pt on local filesystem.""")
	parser.add_argument("""pytorch_dump_folder_path""", default=None, type=str, help="""Path to the output PyTorch model.""")
	__snake_case				:      int   		=			parser.parse_args()
	__snake_case				:      Any   		=			convert_fairseq_mamaaa_checkpoint_from_disk(args.fairseq_pathß)
	model.save_pretrained(args.pytorch_dump_folder_path)


